The Art and Science of Analytics

It all started with a simple question: “Mom, what do you do at work?” As it usually is, the most complex questions are often detailed in the simplest of words. My response had to be simple enough not to scare a 16-year-old, but at the same time, inspiring to a future university candidate looking to choose her trade. So, I replied, “I am a Data Scientist. I carefully collect and select data, process it using mathematical models to describe, identify and solve business problems.” The response was something I would not have expected in a million years. She said, “That’s exactly what I do with art. I carefully select colors and the medium, process it with geometric shapes and structures, and express, identify or try solving an issue.”

Then, this rather instigated a burning question in my mind, “What is my occupational identity? Am I a scientist or an artist?”

What is my occupational identity? Am I a scientist or an artist?

According to Forbes, there are 2.5 quintillion bytes of data created each day, with 90 percent of the data in the world generated in the past two years. We are in an age of a virtuous cycle of data and technology. Technology has enabled us to collect, mine and analyze petabytes of data in minutes, or even seconds, in real time. Use of machine learning and artificial intelligence techniques has allowed us to make better decisions each time. Every organization wants to take advantage of this data explosion, to become a data driven decision maker, and to such an extent the words machine learning and artificial intelligence have become branding tools. Though these are major capabilities to building an analytics practice, they are also very important pieces of a bigger puzzle.

Analytics is an expansive field which demands multiple skill sets – proficiency in applied mathematics, applied technology and deep domain expertise. Data Scientists possess the knowledge of applied Mathematics (Statistics or Econometrics) and technologies to implement ML, AI and other Data engineering techniques. These are conceptual skill sets that are not exclusive to the industries.

With the goal of data analytics to identify or solve a business problem, silos is one luxury a data scientist cannot afford. They should partner with the experts and decision makers to define the problem, design the analyses, formulate hypotheses and interpret/derive insights. A Data Scientist must assume a consultative approach, donning roles of moderator or interviewer in order to extract the domain expertise. This requires understanding the psyche of people and what motivates them, in order to harvest their knowledge.

The science is to manage the data, but the art is to manage people. A scientific approach helps streamline the process of problem identification, design, data analysis and communication. An artistic approach helps to be creative in execution and conclusion. The science lies in the methodology and guidelines in form of laws, principles, rules and modeling techniques whereas the art lies in the understanding of those guidelines, adoption of suitable methods and interpretation of results to derive conclusions.

The science is to manage the data, but the art is to manage people.

Both scientists and artists use creative techniques to uncover hidden messages; messages which can be predictive, alleviating uncertainty and providing direction to possible future, or provide a simple explanation of a situation. In order to produce effective analyses and translate it into actionable insights, we need a collaborative learning approach between sciences and the arts. In sum, who am I? Maybe, a Data Scientist and a Data Artist.

Sushma has 13 years of Marketing Research, Quantitative Analysis and Strategy Consulting experience in the Information Technology industry. Her professional background is a unique blend of, expertise in Marketing processes, Data Sciences and Business Economics. She currently works as VP – Analytics and Data Science, with PureSpectrum and also teaches Marketing Research and Statistics to Graduate students at California Lutheran University, part time. She has also worked with IBM Corporation and GE Capital International Services in the past.

At PureSpectrum, Sushma is responsible for building a data centric culture for the company, incorporating machine learning technologies and data driven decision making into products. At IBM Market Development and Insights organization, Sushma was responsible for implementing strategic initiatives around Customer Experience Analytics – building Statistical models and translating into actionable insights, providing data driven recommendations to the Leadership. Highlights of her career include working as a Market Development Advisor to the integration teams for three major acquisitions and co-leading a pilot project integrating IBM AI system, Watson, for content and search optimization.

She is also a soft skills trainer, training marketers around the world in Storyboarding and Presentation skills, moderated Executive decision making workshops based on Design Thinking concepts, and conducted interviews with C-level audience for market and impact studies. Her interests include Artificial Intelligence and Machine Learning, Quantitative Analysis and Buyer behavior.